Logistics ops · Production
project44 Ocean Visibility tracks Maersk containers with ML-powered predictive ETAs
The problem
Supply chain teams lack real-time visibility into container shipments and cannot proactively identify delays, trans-shipment stalls, or detention and demurrage fee exposure across carriers and forwarders.
Workflow diagram · grounded in source
1
Container number input
trigger
“allows you to track your Maersk containers using the MBL / BL Number (Master Bill of Lading) or Container Number”
2
ML status and ETA update
ai_action
“Our machine learning algorithms automatically update shipment status and predictive ETAs in real-time”
3
Delay and D&D identification
output
“swiftly identify container shipments that are delayed or stuck in trans-shipment, incurring D&D fees”
4
Real-time visibility delivery
output
“providing you and your customers with the most precise information available”
Reported outcome
(not stated)
Reported metrics
Information precisionmost precise information available
Network visibilityunmatched visibility from origin to final destination
Reported stack
Ocean Visibilitymachine learning algorithms
Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Ocean Visibility, machine learning algorithms.
What results were reported?
Information precision: most precise information available; Network visibility: unmatched visibility from origin to final destination (source-reported, not independently verified).
How is this logistics ops AI workflow structured?
Container number input → ML status and ETA update → Delay and D&D identification → Real-time visibility delivery.